Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations451 204
Missing cells1 411 247
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory619.4 MiB
Average record size in memory1.4 KiB

Variable types

Categorical10
Text9
Numeric12
Boolean1
Unsupported1

Alerts

obs_collection has constant value "JWST" Constant
project has constant value "JWST" Constant
calib_level is highly overall correlated with dataproduct_type and 4 other fieldsHigh correlation
dataRights is highly overall correlated with sequence_number and 1 other fieldsHigh correlation
dataproduct_type is highly overall correlated with calib_level and 2 other fieldsHigh correlation
instrument_name is highly overall correlated with calib_level and 3 other fieldsHigh correlation
intentType is highly overall correlated with instrument_name and 3 other fieldsHigh correlation
objID is highly overall correlated with wavelength_regionHigh correlation
obsid is highly overall correlated with dataproduct_type and 3 other fieldsHigh correlation
proposal_id is highly overall correlated with calib_level and 4 other fieldsHigh correlation
proposal_type is highly overall correlated with intentTypeHigh correlation
provenance_name is highly overall correlated with calib_level and 6 other fieldsHigh correlation
sequence_number is highly overall correlated with calib_level and 3 other fieldsHigh correlation
t_max is highly overall correlated with proposal_id and 3 other fieldsHigh correlation
t_min is highly overall correlated with proposal_id and 3 other fieldsHigh correlation
t_obs_release is highly overall correlated with dataRights and 5 other fieldsHigh correlation
wavelength_region is highly overall correlated with objID and 1 other fieldsHigh correlation
intentType is highly imbalanced (64.5%) Imbalance
provenance_name is highly imbalanced (53.6%) Imbalance
wavelength_region is highly imbalanced (74.7%) Imbalance
dataRights is highly imbalanced (65.2%) Imbalance
mtFlag is highly imbalanced (94.0%) Imbalance
filters has 5133 (1.1%) missing values Missing
wavelength_region has 6518 (1.4%) missing values Missing
target_classification has 293581 (65.1%) missing values Missing
t_min has 44349 (9.8%) missing values Missing
t_max has 44349 (9.8%) missing values Missing
em_min has 9190 (2.0%) missing values Missing
em_max has 9190 (2.0%) missing values Missing
t_obs_release has 44349 (9.8%) missing values Missing
sequence_number has 406855 (90.2%) missing values Missing
jpegURL has 45464 (10.1%) missing values Missing
dataURL has 50719 (11.2%) missing values Missing
srcDen has 451204 (100.0%) missing values Missing
obs_id has unique values Unique
obsid has unique values Unique
objID has unique values Unique
srcDen is an unsupported type, check if it needs cleaning or further analysis Unsupported
s_ra has 6212 (1.4%) zeros Zeros
s_dec has 6212 (1.4%) zeros Zeros

Reproduction

Analysis started2025-04-05 13:36:53.321495
Analysis finished2025-04-05 13:38:21.026200
Duration1 minute and 27.7 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

intentType
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.2 MiB
science
420884 
calibration
 
30320

Length

Max length11
Median length7
Mean length7.2687919
Min length7

Characters and Unicode

Total characters3 279 708
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowscience
2nd rowscience
3rd rowscience
4th rowscience
5th rowscience

Common Values

ValueCountFrequency (%)
science 420884
93.3%
calibration 30320
 
6.7%

Length

2025-04-05T14:38:21.121084image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:21.221719image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
science 420884
93.3%
calibration 30320
 
6.7%

Most occurring characters

ValueCountFrequency (%)
c 872088
26.6%
e 841768
25.7%
i 481524
14.7%
n 451204
13.8%
s 420884
12.8%
a 60640
 
1.8%
l 30320
 
0.9%
b 30320
 
0.9%
r 30320
 
0.9%
t 30320
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3279708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 872088
26.6%
e 841768
25.7%
i 481524
14.7%
n 451204
13.8%
s 420884
12.8%
a 60640
 
1.8%
l 30320
 
0.9%
b 30320
 
0.9%
r 30320
 
0.9%
t 30320
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3279708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 872088
26.6%
e 841768
25.7%
i 481524
14.7%
n 451204
13.8%
s 420884
12.8%
a 60640
 
1.8%
l 30320
 
0.9%
b 30320
 
0.9%
r 30320
 
0.9%
t 30320
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3279708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 872088
26.6%
e 841768
25.7%
i 481524
14.7%
n 451204
13.8%
s 420884
12.8%
a 60640
 
1.8%
l 30320
 
0.9%
b 30320
 
0.9%
r 30320
 
0.9%
t 30320
 
0.9%

obs_collection
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
JWST
451204 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1 804 816
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJWST
2nd rowJWST
3rd rowJWST
4th rowJWST
5th rowJWST

Common Values

ValueCountFrequency (%)
JWST 451204
100.0%

Length

2025-04-05T14:38:21.328462image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:21.419106image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
jwst 451204
100.0%

Most occurring characters

ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

provenance_name
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.9 MiB
CALJWST
406855 
APT
44349 

Length

Max length7
Median length7
Mean length6.6068386
Min length3

Characters and Unicode

Total characters2 981 032
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCALJWST
2nd rowCALJWST
3rd rowCALJWST
4th rowCALJWST
5th rowCALJWST

Common Values

ValueCountFrequency (%)
CALJWST 406855
90.2%
APT 44349
 
9.8%

Length

2025-04-05T14:38:21.527562image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:21.630652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
caljwst 406855
90.2%
apt 44349
 
9.8%

Most occurring characters

ValueCountFrequency (%)
A 451204
15.1%
T 451204
15.1%
L 406855
13.6%
C 406855
13.6%
J 406855
13.6%
W 406855
13.6%
S 406855
13.6%
P 44349
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2981032
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 451204
15.1%
T 451204
15.1%
L 406855
13.6%
C 406855
13.6%
J 406855
13.6%
W 406855
13.6%
S 406855
13.6%
P 44349
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2981032
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 451204
15.1%
T 451204
15.1%
L 406855
13.6%
C 406855
13.6%
J 406855
13.6%
W 406855
13.6%
S 406855
13.6%
P 44349
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2981032
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 451204
15.1%
T 451204
15.1%
L 406855
13.6%
C 406855
13.6%
J 406855
13.6%
W 406855
13.6%
S 406855
13.6%
P 44349
 
1.5%

instrument_name
Categorical

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.9 MiB
NIRCAM/GRISM
124565 
NIRSPEC/MSA
119108 
NIRISS/WFSS
72372 
NIRCAM/IMAGE
69282 
MIRI/IMAGE
15204 
Other values (17)
50673 

Length

Max length14
Median length13
Mean length11.300137
Min length3

Characters and Unicode

Total characters5 098 667
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNIRCAM/GRISM
2nd rowNIRCAM/GRISM
3rd rowNIRSPEC/MSA
4th rowNIRSPEC/MSA
5th rowNIRISS/WFSS

Common Values

ValueCountFrequency (%)
NIRCAM/GRISM 124565
27.6%
NIRSPEC/MSA 119108
26.4%
NIRISS/WFSS 72372
16.0%
NIRCAM/IMAGE 69282
15.4%
MIRI/IMAGE 15204
 
3.4%
NIRSPEC/IFU 11601
 
2.6%
MIRI/IFU 9291
 
2.1%
NIRSPEC/SLIT 7163
 
1.6%
NIRSPEC 6064
 
1.3%
NIRISS/IMAGE 5636
 
1.2%
Other values (12) 10918
 
2.4%

Length

2025-04-05T14:38:21.755320image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nircam/grism 124565
27.6%
nirspec/msa 119108
26.4%
niriss/wfss 72372
16.0%
nircam/image 69282
15.4%
miri/image 15204
 
3.4%
nirspec/ifu 11601
 
2.6%
miri/ifu 9291
 
2.1%
nirspec/slit 7163
 
1.6%
nirspec 6064
 
1.3%
niriss/image 5636
 
1.2%
Other values (12) 10918
 
2.4%

Most occurring characters

ValueCountFrequency (%)
I 801828
15.7%
S 704876
13.8%
R 579762
11.4%
M 560899
11.0%
/ 445077
8.7%
N 426125
8.4%
A 409695
8.0%
C 348248
6.8%
E 235046
 
4.6%
G 219493
 
4.3%
Other values (10) 367618
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5098667
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 801828
15.7%
S 704876
13.8%
R 579762
11.4%
M 560899
11.0%
/ 445077
8.7%
N 426125
8.4%
A 409695
8.0%
C 348248
6.8%
E 235046
 
4.6%
G 219493
 
4.3%
Other values (10) 367618
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5098667
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 801828
15.7%
S 704876
13.8%
R 579762
11.4%
M 560899
11.0%
/ 445077
8.7%
N 426125
8.4%
A 409695
8.0%
C 348248
6.8%
E 235046
 
4.6%
G 219493
 
4.3%
Other values (10) 367618
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5098667
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 801828
15.7%
S 704876
13.8%
R 579762
11.4%
M 560899
11.0%
/ 445077
8.7%
N 426125
8.4%
A 409695
8.0%
C 348248
6.8%
E 235046
 
4.6%
G 219493
 
4.3%
Other values (10) 367618
7.2%

project
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size22.8 MiB
JWST
451204 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1 804 816
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJWST
2nd rowJWST
3rd rowJWST
4th rowJWST
5th rowJWST

Common Values

ValueCountFrequency (%)
JWST 451204
100.0%

Length

2025-04-05T14:38:21.911631image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:22.000188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
jwst 451204
100.0%

Most occurring characters

ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1804816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
J 451204
25.0%
W 451204
25.0%
S 451204
25.0%
T 451204
25.0%

filters
Text

Missing 

Distinct305
Distinct (%)0.1%
Missing5133
Missing (%)1.1%
Memory size25.6 MiB
2025-04-05T14:38:22.327089image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length25
Median length12
Mean length10.743886
Min length3

Characters and Unicode

Total characters4 792 536
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowF322W2;GRISMR
2nd rowF356W;GRISMR
3rd rowF290LP;G395M
4th rowF290LP;G395H
5th rowGR150R;F150W
ValueCountFrequency (%)
f444w;grismr 51431
 
11.5%
clear;prism 48531
 
10.9%
f356w;grismr 22863
 
5.1%
f290lp;g395m 21035
 
4.7%
f170lp;g235h 17172
 
3.8%
gr150r;f115w 16482
 
3.7%
f170lp;g235m 15751
 
3.5%
f290lp;g395h 15148
 
3.4%
f444w;grismc 14082
 
3.2%
gr150r;f200w 12052
 
2.7%
Other values (295) 211524
47.4%
2025-04-05T14:38:22.795281image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 433384
 
9.0%
F 401692
 
8.4%
0 392536
 
8.2%
; 370516
 
7.7%
G 287412
 
6.0%
1 285289
 
6.0%
M 276179
 
5.8%
4 268527
 
5.6%
W 267003
 
5.6%
5 261406
 
5.5%
Other values (26) 1548592
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4792536
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 433384
 
9.0%
F 401692
 
8.4%
0 392536
 
8.2%
; 370516
 
7.7%
G 287412
 
6.0%
1 285289
 
6.0%
M 276179
 
5.8%
4 268527
 
5.6%
W 267003
 
5.6%
5 261406
 
5.5%
Other values (26) 1548592
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4792536
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 433384
 
9.0%
F 401692
 
8.4%
0 392536
 
8.2%
; 370516
 
7.7%
G 287412
 
6.0%
1 285289
 
6.0%
M 276179
 
5.8%
4 268527
 
5.6%
W 267003
 
5.6%
5 261406
 
5.5%
Other values (26) 1548592
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4792536
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 433384
 
9.0%
F 401692
 
8.4%
0 392536
 
8.2%
; 370516
 
7.7%
G 287412
 
6.0%
1 285289
 
6.0%
M 276179
 
5.8%
4 268527
 
5.6%
W 267003
 
5.6%
5 261406
 
5.5%
Other values (26) 1548592
32.3%

wavelength_region
Categorical

High correlation  Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing6518
Missing (%)1.4%
Memory size24.5 MiB
INFRARED
425819 
Infrared
 
18867

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3 557 488
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINFRARED
2nd rowINFRARED
3rd rowINFRARED
4th rowINFRARED
5th rowINFRARED

Common Values

ValueCountFrequency (%)
INFRARED 425819
94.4%
Infrared 18867
 
4.2%
(Missing) 6518
 
1.4%

Length

2025-04-05T14:38:22.942487image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:23.068711image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
infrared 444686
100.0%

Most occurring characters

ValueCountFrequency (%)
R 851638
23.9%
I 444686
12.5%
N 425819
12.0%
F 425819
12.0%
A 425819
12.0%
E 425819
12.0%
D 425819
12.0%
r 37734
 
1.1%
n 18867
 
0.5%
f 18867
 
0.5%
Other values (3) 56601
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3557488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 851638
23.9%
I 444686
12.5%
N 425819
12.0%
F 425819
12.0%
A 425819
12.0%
E 425819
12.0%
D 425819
12.0%
r 37734
 
1.1%
n 18867
 
0.5%
f 18867
 
0.5%
Other values (3) 56601
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3557488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 851638
23.9%
I 444686
12.5%
N 425819
12.0%
F 425819
12.0%
A 425819
12.0%
E 425819
12.0%
D 425819
12.0%
r 37734
 
1.1%
n 18867
 
0.5%
f 18867
 
0.5%
Other values (3) 56601
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3557488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 851638
23.9%
I 444686
12.5%
N 425819
12.0%
F 425819
12.0%
A 425819
12.0%
E 425819
12.0%
D 425819
12.0%
r 37734
 
1.1%
n 18867
 
0.5%
f 18867
 
0.5%
Other values (3) 56601
 
1.6%
Distinct6645
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.6 MiB
2025-04-05T14:38:23.346906image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length31
Median length28
Mean length12.76904
Min length2

Characters and Unicode

Total characters5 761 442
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique661 ?
Unique (%)0.1%

Sample

1st rowIRAS-05248-7007
2nd rowJ1120+0641
3rd rowCEERS-FULL-V2
4th rowGOODSS2009
5th rowABELL2744
ValueCountFrequency (%)
unknown 122786
27.2%
gs-medium-hst 8754
 
1.9%
1181-merged-apt-clean-clean 7656
 
1.7%
iras-05248-7007 7149
 
1.6%
abell370 5688
 
1.3%
p330-e 5498
 
1.2%
macsj0416.1-2403 5366
 
1.2%
macsj0417.5-1154 4964
 
1.1%
macsj1423.8+2404 4843
 
1.1%
macsj1149+2223 4609
 
1.0%
Other values (6591) 273893
60.7%
2025-04-05T14:38:23.846718image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 465882
 
8.1%
- 362492
 
6.3%
2 259831
 
4.5%
1 244673
 
4.2%
0 243060
 
4.2%
S 226778
 
3.9%
O 195402
 
3.4%
E 181981
 
3.2%
3 177485
 
3.1%
U 166549
 
2.9%
Other values (57) 3237309
56.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5761442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 465882
 
8.1%
- 362492
 
6.3%
2 259831
 
4.5%
1 244673
 
4.2%
0 243060
 
4.2%
S 226778
 
3.9%
O 195402
 
3.4%
E 181981
 
3.2%
3 177485
 
3.1%
U 166549
 
2.9%
Other values (57) 3237309
56.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5761442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 465882
 
8.1%
- 362492
 
6.3%
2 259831
 
4.5%
1 244673
 
4.2%
0 243060
 
4.2%
S 226778
 
3.9%
O 195402
 
3.4%
E 181981
 
3.2%
3 177485
 
3.1%
U 166549
 
2.9%
Other values (57) 3237309
56.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5761442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 465882
 
8.1%
- 362492
 
6.3%
2 259831
 
4.5%
1 244673
 
4.2%
0 243060
 
4.2%
S 226778
 
3.9%
O 195402
 
3.4%
E 181981
 
3.2%
3 177485
 
3.1%
U 166549
 
2.9%
Other values (57) 3237309
56.2%

target_classification
Text

Missing 

Distinct496
Distinct (%)0.3%
Missing293581
Missing (%)65.1%
Memory size21.9 MiB
2025-04-05T14:38:24.260106image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length130
Median length110
Mean length37.063709
Min length11

Characters and Unicode

Total characters5 842 093
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowCalibration; Spectrophotometric
2nd rowGalaxy; Quasars
3rd rowClusters of Galaxies; Abell clusters
4th rowClusters of Galaxies; Abell clusters; Rich clusters
5th rowGalaxy; High-redshift galaxies; Quasars
ValueCountFrequency (%)
clusters 86600
 
12.0%
galaxies 75305
 
10.5%
of 38283
 
5.3%
galaxy 34622
 
4.8%
calibration 33229
 
4.6%
rich 29657
 
4.1%
star 24086
 
3.3%
field 21497
 
3.0%
sources 20333
 
2.8%
clouds 20064
 
2.8%
Other values (217) 335445
46.6%
2025-04-05T14:38:24.839541image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
561498
 
9.6%
a 511567
 
8.8%
s 496937
 
8.5%
e 452524
 
7.7%
l 424253
 
7.3%
i 387827
 
6.6%
r 382913
 
6.6%
t 364504
 
6.2%
; 240120
 
4.1%
o 220224
 
3.8%
Other values (44) 1799726
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5842093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
561498
 
9.6%
a 511567
 
8.8%
s 496937
 
8.5%
e 452524
 
7.7%
l 424253
 
7.3%
i 387827
 
6.6%
r 382913
 
6.6%
t 364504
 
6.2%
; 240120
 
4.1%
o 220224
 
3.8%
Other values (44) 1799726
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5842093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
561498
 
9.6%
a 511567
 
8.8%
s 496937
 
8.5%
e 452524
 
7.7%
l 424253
 
7.3%
i 387827
 
6.6%
r 382913
 
6.6%
t 364504
 
6.2%
; 240120
 
4.1%
o 220224
 
3.8%
Other values (44) 1799726
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5842093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
561498
 
9.6%
a 511567
 
8.8%
s 496937
 
8.5%
e 452524
 
7.7%
l 424253
 
7.3%
i 387827
 
6.6%
r 382913
 
6.6%
t 364504
 
6.2%
; 240120
 
4.1%
o 220224
 
3.8%
Other values (44) 1799726
30.8%

obs_id
Text

Unique 

Distinct451204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size38.3 MiB
2025-04-05T14:38:25.423884image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length67
Median length61
Mean length40.053362
Min length27

Characters and Unicode

Total characters18 072 237
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique451 204 ?
Unique (%)100.0%

Sample

1st rowjw01076-o113_s000002791_nircam_f322w2-grismr
2nd rowjw01243-o004_s000001452_nircam_f356w-grismr
3rd rowjw04233-o005_s000041321_nirspec_f290lp-g395m
4th rowjw01212-o009_b000000073_nirspec_f290lp-g395h
5th rowjw01324-o007_s000001336_niriss_f150w-gr150r
ValueCountFrequency (%)
jw01324-o007_s000001336_niriss_f150w-gr150r 1
 
< 0.1%
jw01095-o004_s000000328_niriss_f090w-gr150c 1
 
< 0.1%
jw01076-o113_s000002791_nircam_f322w2-grismr 1
 
< 0.1%
jw01243-o004_s000001452_nircam_f356w-grismr 1
 
< 0.1%
jw01181-c1001_s000064685_nirspec_f170lp-g235m 1
 
< 0.1%
jw01181-o198_s000037480_nirspec_clear-prism 1
 
< 0.1%
jw06643-o002_v000000001_nirspec_f290lp-g395h 1
 
< 0.1%
jw07213002007_xx10i_00003_miri 1
 
< 0.1%
jw05398-c1024_s000000612_nircam_f444w-grismr 1
 
< 0.1%
jw01345-o013_s000001331_nircam_f356w-grismr 1
 
< 0.1%
Other values (451194) 451194
> 99.9%
2025-04-05T14:38:26.195388image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4140082
22.9%
1 1415147
 
7.8%
_ 1353612
 
7.5%
r 870314
 
4.8%
s 745086
 
4.1%
2 715359
 
4.0%
- 674876
 
3.7%
i 656291
 
3.6%
w 645643
 
3.6%
4 619081
 
3.4%
Other values (28) 6236746
34.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18072237
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4140082
22.9%
1 1415147
 
7.8%
_ 1353612
 
7.5%
r 870314
 
4.8%
s 745086
 
4.1%
2 715359
 
4.0%
- 674876
 
3.7%
i 656291
 
3.6%
w 645643
 
3.6%
4 619081
 
3.4%
Other values (28) 6236746
34.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18072237
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4140082
22.9%
1 1415147
 
7.8%
_ 1353612
 
7.5%
r 870314
 
4.8%
s 745086
 
4.1%
2 715359
 
4.0%
- 674876
 
3.7%
i 656291
 
3.6%
w 645643
 
3.6%
4 619081
 
3.4%
Other values (28) 6236746
34.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18072237
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4140082
22.9%
1 1415147
 
7.8%
_ 1353612
 
7.5%
r 870314
 
4.8%
s 745086
 
4.1%
2 715359
 
4.0%
- 674876
 
3.7%
i 656291
 
3.6%
w 645643
 
3.6%
4 619081
 
3.4%
Other values (28) 6236746
34.5%

s_ra
Real number (ℝ)

Zeros 

Distinct58497
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.34876
Minimum0
Maximum360
Zeros6212
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:26.384149image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.2341347
Q153.153721
median150.15524
Q3214.77293
95-th percentile296.13988
Maximum360
Range360
Interquartile range (IQR)161.61921

Descriptive statistics

Standard deviation93.630951
Coefficient of variation (CV)0.66241082
Kurtosis-0.88561527
Mean141.34876
Median Absolute Deviation (MAD)85.758788
Skewness0.30069023
Sum63777128
Variance8766.7549
MonotonicityNot monotonic
2025-04-05T14:38:26.595981image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.15372125 8754
 
1.9%
189.1338629 7647
 
1.7%
81.08646708 7054
 
1.6%
0 6212
 
1.4%
189.2135646 6085
 
1.3%
64.03904167 5334
 
1.2%
39.97535417 5151
 
1.1%
64.39645208 4964
 
1.1%
215.9490833 4843
 
1.1%
177.399375 4601
 
1.0%
Other values (58487) 390559
86.6%
ValueCountFrequency (%)
0 6212
1.4%
4.765000274 × 10-61
 
< 0.1%
7.954999984 × 10-662
 
< 0.1%
9.162498666 × 10-62
 
< 0.1%
1.01775 × 10-51
 
< 0.1%
1.431000001 × 10-53
 
< 0.1%
1.89325 × 10-542
 
< 0.1%
1.925249998 × 10-52
 
< 0.1%
2.655000001 × 10-5142
 
< 0.1%
3.157249999 × 10-52
 
< 0.1%
ValueCountFrequency (%)
359.9999999 1
 
< 0.1%
359.9999998 3
 
< 0.1%
359.9999993 3
 
< 0.1%
359.9999968 53
< 0.1%
359.9999857 23
< 0.1%
359.9999855 1
 
< 0.1%
359.9999823 3
 
< 0.1%
359.9999809 26
< 0.1%
359.9999807 12
 
< 0.1%
359.9999771 42
< 0.1%

s_dec
Real number (ℝ)

Zeros 

Distinct58839
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2287342
Minimum-88.862471
Maximum88.705989
Zeros6212
Zeros (%)1.4%
Negative219189
Negative (%)48.6%
Memory size3.4 MiB
2025-04-05T14:38:26.765932image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum-88.862471
5-th percentile-69.334863
Q1-27.780369
median1.64 × 10-5
Q342.095843
95-th percentile62.87523
Maximum88.705989
Range177.56846
Interquartile range (IQR)69.876212

Descriptive statistics

Standard deviation40.421513
Coefficient of variation (CV)9.5587737
Kurtosis-0.92882901
Mean4.2287342
Median Absolute Deviation (MAD)27.872041
Skewness0.0033605901
Sum1908021.8
Variance1633.8987
MonotonicityNot monotonic
2025-04-05T14:38:26.934184image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-27.78036944 8754
 
1.9%
62.26666111 7656
 
1.7%
-70.08377778 7054
 
1.6%
0 6212
 
1.4%
62.21797222 6085
 
1.3%
-24.07236111 5334
 
1.2%
-1.576044444 5019
 
1.1%
-11.91067778 4964
 
1.1%
24.0777 4843
 
1.1%
22.39827222 4601
 
1.0%
Other values (58829) 390682
86.6%
ValueCountFrequency (%)
-88.86247145 1
 
< 0.1%
-88.86247145 5
 
< 0.1%
-88.86247145 1
 
< 0.1%
-88.86247145 3
 
< 0.1%
-88.86247145 2
 
< 0.1%
-86.69708333 6
 
< 0.1%
-86.6319 30
< 0.1%
-86.22233644 1
 
< 0.1%
-86.22233644 1
 
< 0.1%
-86.22233644 4
 
< 0.1%
ValueCountFrequency (%)
88.70598889 12
< 0.1%
87.89033183 1
 
< 0.1%
86.85841897 1
 
< 0.1%
86.8549542 1
 
< 0.1%
86.84643709 1
 
< 0.1%
86.80237303 1
 
< 0.1%
86.80031442 1
 
< 0.1%
86.79533043 1
 
< 0.1%
86.6034763 1
 
< 0.1%
86.6034763 1
 
< 0.1%

dataproduct_type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.2 MiB
spectrum
336227 
image
114977 

Length

Max length8
Median length8
Mean length7.235532
Min length5

Characters and Unicode

Total characters3 264 701
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowspectrum
2nd rowspectrum
3rd rowspectrum
4th rowspectrum
5th rowspectrum

Common Values

ValueCountFrequency (%)
spectrum 336227
74.5%
image 114977
 
25.5%

Length

2025-04-05T14:38:27.108886image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:27.248520image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
spectrum 336227
74.5%
image 114977
 
25.5%

Most occurring characters

ValueCountFrequency (%)
e 451204
13.8%
m 451204
13.8%
s 336227
10.3%
p 336227
10.3%
c 336227
10.3%
r 336227
10.3%
t 336227
10.3%
u 336227
10.3%
i 114977
 
3.5%
a 114977
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3264701
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 451204
13.8%
m 451204
13.8%
s 336227
10.3%
p 336227
10.3%
c 336227
10.3%
r 336227
10.3%
t 336227
10.3%
u 336227
10.3%
i 114977
 
3.5%
a 114977
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3264701
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 451204
13.8%
m 451204
13.8%
s 336227
10.3%
p 336227
10.3%
c 336227
10.3%
r 336227
10.3%
t 336227
10.3%
u 336227
10.3%
i 114977
 
3.5%
a 114977
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3264701
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 451204
13.8%
m 451204
13.8%
s 336227
10.3%
p 336227
10.3%
c 336227
10.3%
r 336227
10.3%
t 336227
10.3%
u 336227
10.3%
i 114977
 
3.5%
a 114977
 
3.5%
Distinct756
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
2025-04-05T14:38:27.610473image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length37
Median length31
Mean length16.449318
Min length7

Characters and Unicode

Total characters7 421 998
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowPirzkal, Norbert
2nd rowLilly, Simon J.
3rd rowde Graaff, Anna G
4th rowLuetzgendorf, Nora
5th rowTreu, Tommaso L.
ValueCountFrequency (%)
j 63794
 
5.9%
jeyhan 37362
 
3.4%
kartaltepe 37362
 
3.4%
a 33730
 
3.1%
daniel 32125
 
2.9%
eisenstein 29317
 
2.7%
matthew 28510
 
2.6%
malkan 27917
 
2.6%
chris 26168
 
2.4%
willott 25936
 
2.4%
Other values (1299) 747353
68.6%
2025-04-05T14:38:28.177315image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 690717
 
9.3%
638370
 
8.6%
e 611895
 
8.2%
i 557249
 
7.5%
, 451192
 
6.1%
n 421861
 
5.7%
r 415830
 
5.6%
t 363479
 
4.9%
l 350871
 
4.7%
o 280382
 
3.8%
Other values (46) 2640152
35.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7421998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 690717
 
9.3%
638370
 
8.6%
e 611895
 
8.2%
i 557249
 
7.5%
, 451192
 
6.1%
n 421861
 
5.7%
r 415830
 
5.6%
t 363479
 
4.9%
l 350871
 
4.7%
o 280382
 
3.8%
Other values (46) 2640152
35.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7421998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 690717
 
9.3%
638370
 
8.6%
e 611895
 
8.2%
i 557249
 
7.5%
, 451192
 
6.1%
n 421861
 
5.7%
r 415830
 
5.6%
t 363479
 
4.9%
l 350871
 
4.7%
o 280382
 
3.8%
Other values (46) 2640152
35.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7421998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 690717
 
9.3%
638370
 
8.6%
e 611895
 
8.2%
i 557249
 
7.5%
, 451192
 
6.1%
n 421861
 
5.7%
r 415830
 
5.6%
t 363479
 
4.9%
l 350871
 
4.7%
o 280382
 
3.8%
Other values (46) 2640152
35.6%

calib_level
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size21.6 MiB
3
333412 
2
73443 
-1
44349 

Length

Max length2
Median length1
Mean length1.0982904
Min length1

Characters and Unicode

Total characters495 553
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 333412
73.9%
2 73443
 
16.3%
-1 44349
 
9.8%

Length

2025-04-05T14:38:28.360376image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:28.462900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
3 333412
73.9%
2 73443
 
16.3%
1 44349
 
9.8%

Most occurring characters

ValueCountFrequency (%)
3 333412
67.3%
2 73443
 
14.8%
- 44349
 
8.9%
1 44349
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 495553
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 333412
67.3%
2 73443
 
14.8%
- 44349
 
8.9%
1 44349
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 495553
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 333412
67.3%
2 73443
 
14.8%
- 44349
 
8.9%
1 44349
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 495553
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 333412
67.3%
2 73443
 
14.8%
- 44349
 
8.9%
1 44349
 
8.9%

t_min
Real number (ℝ)

High correlation  Missing 

Distinct57208
Distinct (%)14.1%
Missing44349
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean60220.609
Minimum59607.6
Maximum60751.238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:28.603583image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum59607.6
5-th percentile59699.224
Q159950.219
median60197.646
Q360516.05
95-th percentile60720.476
Maximum60751.238
Range1143.6384
Interquartile range (IQR)565.83176

Descriptive statistics

Standard deviation332.22186
Coefficient of variation (CV)0.005516747
Kurtosis-1.2213862
Mean60220.609
Median Absolute Deviation (MAD)267.67546
Skewness0.022194892
Sum2.4501056 × 1010
Variance110371.37
MonotonicityNot monotonic
2025-04-05T14:38:28.788883image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59979.85261 4555
 
1.0%
60638.86792 2038
 
0.5%
60361.96889 1918
 
0.4%
60185.82039 1717
 
0.4%
60661.76733 1636
 
0.4%
60516.05043 1473
 
0.3%
60325.95101 1456
 
0.3%
60530.95249 1409
 
0.3%
60382.36782 1369
 
0.3%
60565.65457 1359
 
0.3%
Other values (57198) 387925
86.0%
(Missing) 44349
 
9.8%
ValueCountFrequency (%)
59607.59978 1
< 0.1%
59607.59979 1
< 0.1%
59607.5998 1
< 0.1%
59607.60894 1
< 0.1%
59607.60895 1
< 0.1%
59607.60895 1
< 0.1%
59607.62542 1
< 0.1%
59607.62542 1
< 0.1%
59607.62543 1
< 0.1%
59607.63133 1
< 0.1%
ValueCountFrequency (%)
60751.2382 8
< 0.1%
60751.2382 2
 
< 0.1%
60751.22677 7
< 0.1%
60751.22677 3
 
< 0.1%
60751.21534 5
< 0.1%
60751.21534 5
< 0.1%
60751.21076 2
 
< 0.1%
60751.20173 2
 
< 0.1%
60751.1953 2
 
< 0.1%
60751.1578 1
 
< 0.1%

t_max
Real number (ℝ)

High correlation  Missing 

Distinct58085
Distinct (%)14.3%
Missing44349
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean60223.224
Minimum59607.603
Maximum60751.249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:28.964877image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum59607.603
5-th percentile59699.255
Q159952.341
median60198.215
Q360516.391
95-th percentile60720.514
Maximum60751.249
Range1143.6463
Interquartile range (IQR)564.05046

Descriptive statistics

Standard deviation331.0814
Coefficient of variation (CV)0.0054975701
Kurtosis-1.2063157
Mean60223.224
Median Absolute Deviation (MAD)263.96208
Skewness0.014722189
Sum2.450212 × 1010
Variance109614.89
MonotonicityNot monotonic
2025-04-05T14:38:29.146154image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60665.31725 2038
 
0.5%
60362.98763 1918
 
0.4%
60189.14367 1731
 
0.4%
59982.49335 1561
 
0.3%
60566.26025 1359
 
0.3%
60094.48384 1351
 
0.3%
60094.38991 1321
 
0.3%
60288.65439 1248
 
0.3%
60190.321 1184
 
0.3%
60566.23266 1132
 
0.3%
Other values (58075) 392012
86.9%
(Missing) 44349
 
9.8%
ValueCountFrequency (%)
59607.603 1
< 0.1%
59607.603 1
< 0.1%
59607.60301 1
< 0.1%
59607.61295 1
< 0.1%
59607.61344 1
< 0.1%
59607.61345 1
< 0.1%
59607.62619 1
< 0.1%
59607.6262 1
< 0.1%
59607.6262 1
< 0.1%
59607.6321 1
< 0.1%
ValueCountFrequency (%)
60751.24926 8
< 0.1%
60751.24926 2
 
< 0.1%
60751.23783 7
< 0.1%
60751.23783 3
 
< 0.1%
60751.2264 5
< 0.1%
60751.2264 5
< 0.1%
60751.21126 2
 
< 0.1%
60751.20223 2
 
< 0.1%
60751.1958 2
 
< 0.1%
60751.16117 1
 
< 0.1%

t_exptime
Real number (ℝ)

Distinct4082
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2453.2567
Minimum0
Maximum210080.01
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:29.329188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile72.944
Q1291.778
median773.047
Q32188.333
95-th percentile8534.502
Maximum210080.01
Range210080.01
Interquartile range (IQR)1896.555

Descriptive statistics

Standard deviation6833.781
Coefficient of variation (CV)2.7855956
Kurtosis172.26699
Mean2453.2567
Median Absolute Deviation (MAD)622.732
Skewness10.710143
Sum1.1069192 × 109
Variance46700563
MonotonicityNot monotonic
2025-04-05T14:38:29.867034image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
730.1 17455
 
3.9%
472.418 16999
 
3.8%
3063.666 14715
 
3.3%
96.631 13757
 
3.0%
1202.518 12917
 
2.9%
2844.834 11689
 
2.6%
901.889 11569
 
2.6%
311.366 9636
 
2.1%
150.315 9248
 
2.0%
773.047 8497
 
1.9%
Other values (4072) 324722
72.0%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.03 128
< 0.1%
0.045 14
 
< 0.1%
0.075 5
 
< 0.1%
0.137 4
 
< 0.1%
0.15 18
 
< 0.1%
0.151 1
 
< 0.1%
0.18 104
< 0.1%
0.224 6
 
< 0.1%
0.251 13
 
< 0.1%
ValueCountFrequency (%)
210080.01 9
 
< 0.1%
175066.675 2
 
< 0.1%
168064.008 1
 
< 0.1%
166313.34 59
< 0.1%
164916.78 2
 
< 0.1%
147056.007 1
 
< 0.1%
140694.624 3
 
< 0.1%
138234.537 70
< 0.1%
133050.672 20
 
< 0.1%
126048.006 8
 
< 0.1%

em_min
Real number (ℝ)

Missing 

Distinct60
Distinct (%)< 0.1%
Missing9190
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2512.1497
Minimum500
Maximum23500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:30.038295image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile600
Q1800
median1755
Q33177
95-th percentile5000
Maximum23500
Range23000
Interquartile range (IQR)2377

Descriptive statistics

Standard deviation2632.7194
Coefficient of variation (CV)1.0479946
Kurtosis19.463413
Mean2512.1497
Median Absolute Deviation (MAD)1115
Skewness3.7525562
Sum1.1104053 × 109
Variance6931211.5
MonotonicityNot monotonic
2025-04-05T14:38:30.212337image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3880 70096
15.5%
800 68992
15.3%
600 55610
12.3%
2870 37526
8.3%
1660 34157
 
7.6%
3140 30213
 
6.7%
700 23598
 
5.2%
1755 12253
 
2.7%
2430 9626
 
2.1%
1331 9235
 
2.0%
Other values (50) 90708
20.1%
(Missing) 9190
 
2.0%
ValueCountFrequency (%)
500 42
 
< 0.1%
600 55610
12.3%
621 852
 
0.2%
700 23598
 
5.2%
795 4033
 
0.9%
800 68992
15.3%
810 4408
 
1.0%
1000 1790
 
0.4%
1008 1906
 
0.4%
1013 8645
 
1.9%
ValueCountFrequency (%)
23500 791
 
0.2%
20700 303
 
0.1%
18500 2346
0.5%
16500 1457
0.3%
15410 84
 
< 0.1%
15110 471
 
0.1%
13500 2058
0.5%
13340 101
 
< 0.1%
11600 1910
0.4%
11550 178
 
< 0.1%

em_max
Real number (ℝ)

Missing 

Distinct65
Distinct (%)< 0.1%
Missing9190
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean4511.2073
Minimum781
Maximum27900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:30.405875image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum781
5-th percentile1840
Q12300
median4986
Q35100
95-th percentile8800
Maximum27900
Range27119
Interquartile range (IQR)2800

Descriptive statistics

Standard deviation2912.4179
Coefficient of variation (CV)0.64559611
Kurtosis23.47132
Mean4511.2073
Median Absolute Deviation (MAD)314
Skewness4.065646
Sum1.9940168 × 109
Variance8482178.2
MonotonicityNot monotonic
2025-04-05T14:38:30.586920image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4986 73485
16.3%
5000 54012
12.0%
2200 50162
11.1%
5300 49040
10.9%
3980 32133
 
7.1%
2300 23408
 
5.2%
5100 21846
 
4.8%
5140 15786
 
3.5%
2134 11315
 
2.5%
4013 11177
 
2.5%
Other values (55) 99650
22.1%
(Missing) 9190
 
2.0%
ValueCountFrequency (%)
781 407
 
0.1%
1000 191
 
< 0.1%
1005 3256
0.7%
1282 6492
1.4%
1300 1754
 
0.4%
1479 1405
 
0.3%
1500 271
 
0.1%
1668 5504
1.2%
1700 868
 
0.2%
1820 932
 
0.2%
ValueCountFrequency (%)
27900 84
 
< 0.1%
27500 953
0.2%
25300 303
 
0.1%
24480 101
 
< 0.1%
23500 2346
0.5%
20950 178
 
< 0.1%
19500 1457
0.3%
16500 2058
0.5%
15890 471
 
0.1%
14000 1910
0.4%
Distinct1379
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size49.6 MiB
2025-04-05T14:38:30.925180image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length188
Median length143
Mean length66.27392
Min length4

Characters and Unicode

Total characters29 903 058
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)< 0.1%

Sample

1st rowNIRCam LW Grism Baseline Characterization (CAR NIRCam-032)
2nd rowExploring the End of Cosmic Reionization
3rd rowA complete census of the rare, extreme and red: a NIRCam-selected extragalactic community survey with JWST/NIRSpec
4th rowNIRSpec WIDE MOS Survey - GOODS-S
5th rowThrough the looking GLASS: a JWST exploration of galaxy formation and evolution from cosmic dawn to present day
ValueCountFrequency (%)
the 252606
 
6.1%
survey 219101
 
5.3%
of 185369
 
4.5%
105189
 
2.5%
a 95691
 
2.3%
galaxy 77868
 
1.9%
in 66575
 
1.6%
and 63201
 
1.5%
slitless 54822
 
1.3%
parallel 53097
 
1.3%
Other values (2941) 2986948
71.8%
2025-04-05T14:38:31.487384image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3711836
 
12.4%
e 2360348
 
7.9%
a 1826688
 
6.1%
i 1651179
 
5.5%
r 1471142
 
4.9%
t 1345544
 
4.5%
l 1333347
 
4.5%
n 1299316
 
4.3%
o 1289296
 
4.3%
s 1245633
 
4.2%
Other values (78) 12368729
41.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29903058
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3711836
 
12.4%
e 2360348
 
7.9%
a 1826688
 
6.1%
i 1651179
 
5.5%
r 1471142
 
4.9%
t 1345544
 
4.5%
l 1333347
 
4.5%
n 1299316
 
4.3%
o 1289296
 
4.3%
s 1245633
 
4.2%
Other values (78) 12368729
41.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29903058
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3711836
 
12.4%
e 2360348
 
7.9%
a 1826688
 
6.1%
i 1651179
 
5.5%
r 1471142
 
4.9%
t 1345544
 
4.5%
l 1333347
 
4.5%
n 1299316
 
4.3%
o 1289296
 
4.3%
s 1245633
 
4.2%
Other values (78) 12368729
41.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29903058
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3711836
 
12.4%
e 2360348
 
7.9%
a 1826688
 
6.1%
i 1651179
 
5.5%
r 1471142
 
4.9%
t 1345544
 
4.5%
l 1333347
 
4.5%
n 1299316
 
4.3%
o 1289296
 
4.3%
s 1245633
 
4.2%
Other values (78) 12368729
41.4%

t_obs_release
Real number (ℝ)

High correlation  Missing 

Distinct58511
Distinct (%)14.4%
Missing44349
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean60418.278
Minimum59773.625
Maximum61116.384
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:31.660358image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum59773.625
5-th percentile59774.854
Q160229.904
median60462.034
Q360658.848
95-th percentile60842.152
Maximum61116.384
Range1342.7588
Interquartile range (IQR)428.94403

Descriptive statistics

Standard deviation320.73951
Coefficient of variation (CV)0.0053086503
Kurtosis-0.39684129
Mean60418.278
Median Absolute Deviation (MAD)202.7574
Skewness-0.51825629
Sum2.4581479 × 1010
Variance102873.83
MonotonicityNot monotonic
2025-04-05T14:38:31.859915image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59774.85417 33731
 
7.5%
60665.99623 2037
 
0.5%
60733.10417 1918
 
0.4%
60348.04921 1907
 
0.4%
60555.64448 1728
 
0.4%
60663.96287 1636
 
0.4%
60969.69606 1607
 
0.4%
60462.03392 1360
 
0.3%
60567.25258 1355
 
0.3%
60517.77781 1344
 
0.3%
Other values (58501) 358232
79.4%
(Missing) 44349
 
9.8%
ValueCountFrequency (%)
59773.625 859
 
0.2%
59774.54167 1180
 
0.3%
59774.85417 33731
7.5%
59774.98501 9
 
< 0.1%
59775.03186 1
 
< 0.1%
59775.03201 2
 
< 0.1%
59775.38207 1
 
< 0.1%
59775.45307 2
 
< 0.1%
59775.46177 2
 
< 0.1%
59775.47627 1
 
< 0.1%
ValueCountFrequency (%)
61116.38378 9
< 0.1%
61116.26333 1
 
< 0.1%
61116.26321 1
 
< 0.1%
61116.2631 1
 
< 0.1%
61116.26307 1
 
< 0.1%
61116.26292 1
 
< 0.1%
61116.26272 1
 
< 0.1%
61116.26208 1
 
< 0.1%
61116.26167 1
 
< 0.1%
61116.26148 1
 
< 0.1%

proposal_id
Real number (ℝ)

High correlation 

Distinct1433
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3300.6507
Minimum1011
Maximum9287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:32.021500image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1121
Q11213
median2514
Q35398
95-th percentile7169
Maximum9287
Range8276
Interquartile range (IQR)4185

Descriptive statistics

Standard deviation2203.991
Coefficient of variation (CV)0.66774441
Kurtosis-0.78294694
Mean3300.6507
Median Absolute Deviation (MAD)1333
Skewness0.66577167
Sum1.4892668 × 109
Variance4857576.5
MonotonicityNot monotonic
2025-04-05T14:38:32.186842image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5398 36570
 
8.1%
1571 27917
 
6.2%
1208 25650
 
5.7%
6434 20477
 
4.5%
3990 14110
 
3.1%
1181 13773
 
3.1%
1076 11271
 
2.5%
1180 9706
 
2.2%
1187 9204
 
2.0%
4233 8260
 
1.8%
Other values (1423) 274266
60.8%
ValueCountFrequency (%)
1011 6
 
< 0.1%
1012 57
< 0.1%
1014 11
 
< 0.1%
1016 11
 
< 0.1%
1017 40
< 0.1%
1018 48
< 0.1%
1019 74
< 0.1%
1021 26
 
< 0.1%
1022 38
< 0.1%
1023 90
< 0.1%
ValueCountFrequency (%)
9287 288
 
0.1%
9285 204
 
< 0.1%
9265 60
 
< 0.1%
9264 48
 
< 0.1%
9263 816
0.2%
9262 58
 
< 0.1%
9261 24
 
< 0.1%
9260 56
 
< 0.1%
9259 8
 
< 0.1%
9258 36
 
< 0.1%

proposal_type
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing182
Missing (%)< 0.1%
Memory size22.1 MiB
GO
249182 
GTO
113654 
COM
39702 
CAL
30361 
ERS
 
11366
Other values (3)
 
6757

Length

Max length6
Median length2
Mean length2.4621194
Min length2

Characters and Unicode

Total characters1 110 470
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOM
2nd rowGTO
3rd rowGO
4th rowGTO
5th rowERS

Common Values

ValueCountFrequency (%)
GO 249182
55.2%
GTO 113654
25.2%
COM 39702
 
8.8%
CAL 30361
 
6.7%
ERS 11366
 
2.5%
DD 3356
 
0.7%
SURVEY 3314
 
0.7%
ENG 87
 
< 0.1%
(Missing) 182
 
< 0.1%

Length

2025-04-05T14:38:32.361108image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:32.507523image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
go 249182
55.2%
gto 113654
25.2%
com 39702
 
8.8%
cal 30361
 
6.7%
ers 11366
 
2.5%
dd 3356
 
0.7%
survey 3314
 
0.7%
eng 87
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 402538
36.2%
G 362923
32.7%
T 113654
 
10.2%
C 70063
 
6.3%
M 39702
 
3.6%
A 30361
 
2.7%
L 30361
 
2.7%
E 14767
 
1.3%
R 14680
 
1.3%
S 14680
 
1.3%
Other values (5) 16741
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1110470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 402538
36.2%
G 362923
32.7%
T 113654
 
10.2%
C 70063
 
6.3%
M 39702
 
3.6%
A 30361
 
2.7%
L 30361
 
2.7%
E 14767
 
1.3%
R 14680
 
1.3%
S 14680
 
1.3%
Other values (5) 16741
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1110470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 402538
36.2%
G 362923
32.7%
T 113654
 
10.2%
C 70063
 
6.3%
M 39702
 
3.6%
A 30361
 
2.7%
L 30361
 
2.7%
E 14767
 
1.3%
R 14680
 
1.3%
S 14680
 
1.3%
Other values (5) 16741
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1110470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 402538
36.2%
G 362923
32.7%
T 113654
 
10.2%
C 70063
 
6.3%
M 39702
 
3.6%
A 30361
 
2.7%
L 30361
 
2.7%
E 14767
 
1.3%
R 14680
 
1.3%
S 14680
 
1.3%
Other values (5) 16741
 
1.5%

sequence_number
Real number (ℝ)

High correlation  Missing 

Distinct320
Distinct (%)0.7%
Missing406855
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean10.318248
Minimum1
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:32.669591image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile29
Maximum320
Range319
Interquartile range (IQR)7

Descriptive statistics

Standard deviation20.086341
Coefficient of variation (CV)1.9466813
Kurtosis86.559683
Mean10.318248
Median Absolute Deviation (MAD)3
Skewness8.0867585
Sum457604
Variance403.46109
MonotonicityNot monotonic
2025-04-05T14:38:32.826917image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 5167
 
1.1%
3 4709
 
1.0%
4 4480
 
1.0%
1 3608
 
0.8%
5 3274
 
0.7%
6 3097
 
0.7%
7 2857
 
0.6%
8 2703
 
0.6%
9 1942
 
0.4%
10 1487
 
0.3%
Other values (310) 11025
 
2.4%
(Missing) 406855
90.2%
ValueCountFrequency (%)
1 3608
0.8%
2 5167
1.1%
3 4709
1.0%
4 4480
1.0%
5 3274
0.7%
6 3097
0.7%
7 2857
0.6%
8 2703
0.6%
9 1942
 
0.4%
10 1487
 
0.3%
ValueCountFrequency (%)
320 1
< 0.1%
319 1
< 0.1%
318 1
< 0.1%
317 1
< 0.1%
316 1
< 0.1%
315 1
< 0.1%
314 1
< 0.1%
313 1
< 0.1%
312 1
< 0.1%
311 1
< 0.1%
Distinct230120
Distinct (%)51.0%
Missing164
Missing (%)< 0.1%
Memory size73.5 MiB
2025-04-05T14:38:34.110723image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length184
Median length179
Mean length121.82195
Min length97

Characters and Unicode

Total characters54 946 573
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 787 ?
Unique (%)47.6%

Sample

1st rowPOLYGON 81.13980245 -70.10170317 81.03323723 -70.1019282 81.03442975 -70.06577854 81.13830085 -70.06582582 81.13980245 -70.10170317
2nd rowPOLYGON 170.02444323 6.67214966 169.98792647 6.67192467 169.98830271 6.70807468 170.0239607 6.70802746 170.02444323 6.67214966
3rd rowPOLYGON 214.838903171 52.8190293 214.838925626 52.8190293 214.838925626 52.818139786 214.838903171 52.818139786
4th rowPOLYGON 53.049465643 -27.718402892 53.050442906 -27.718402892 53.050442906 -27.718412699 53.049465643 -27.718412699
5th rowPOLYGON 3.61566792 -30.4145566 3.57252534 -30.41437319 3.5729716 -30.37687049 3.61608797 -30.37708431 3.61566792 -30.4145566
ValueCountFrequency (%)
polygon 451040
 
10.2%
64.05920641 9650
 
0.2%
24.09122368 9650
 
0.2%
215.96924299 9086
 
0.2%
24.05883479 9086
 
0.2%
1.59490825 9036
 
0.2%
39.99376963 9036
 
0.2%
64.41526676 8940
 
0.2%
11.92954104 8940
 
0.2%
22.37940712 8468
 
0.2%
Other values (1373934) 3905010
88.0%
2025-04-05T14:38:35.670231image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5149659
9.4%
1 4914110
8.9%
3 4262592
 
7.8%
5 4239668
 
7.7%
6 4198882
 
7.6%
4 4177952
 
7.6%
8 4082425
 
7.4%
9 4062890
 
7.4%
3986902
 
7.3%
. 3986902
 
7.3%
Other values (10) 11884591
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54946573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5149659
9.4%
1 4914110
8.9%
3 4262592
 
7.8%
5 4239668
 
7.7%
6 4198882
 
7.6%
4 4177952
 
7.6%
8 4082425
 
7.4%
9 4062890
 
7.4%
3986902
 
7.3%
. 3986902
 
7.3%
Other values (10) 11884591
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54946573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5149659
9.4%
1 4914110
8.9%
3 4262592
 
7.8%
5 4239668
 
7.7%
6 4198882
 
7.6%
4 4177952
 
7.6%
8 4082425
 
7.4%
9 4062890
 
7.4%
3986902
 
7.3%
. 3986902
 
7.3%
Other values (10) 11884591
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54946573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5149659
9.4%
1 4914110
8.9%
3 4262592
 
7.8%
5 4239668
 
7.7%
6 4198882
 
7.6%
4 4177952
 
7.6%
8 4082425
 
7.4%
9 4062890
 
7.4%
3986902
 
7.3%
. 3986902
 
7.3%
Other values (10) 11884591
21.6%

jpegURL
Text

Missing 

Distinct405716
Distinct (%)> 99.9%
Missing45464
Missing (%)10.1%
Memory size46.4 MiB
2025-04-05T14:38:36.361522image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length93
Median length87
Mean length67.377264
Min length53

Characters and Unicode

Total characters27 337 651
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique405 692 ?
Unique (%)> 99.9%

Sample

1st rowmast:JWST/product/jw01076-o113_s000002791_nircam_f322w2-grismr_cal.jpg
2nd rowmast:JWST/product/jw01243-o004_s000001452_nircam_f356w-grismr_cal.jpg
3rd rowmast:JWST/product/jw04233-o005_s000041321_nirspec_f290lp-g395m_cal.jpg
4th rowmast:JWST/product/jw01212-o009_b000000073_nirspec_f290lp-g395h_cal.jpg
5th rowmast:JWST/product/jw01324-o007_s000001336_niriss_f150w-gr150r_cal.jpg
ValueCountFrequency (%)
mast:jwst/product/jw06627-o003_t001_nircam_clear-f150w2_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01094-o003_t001_niriss_clearp-f380m-sub128_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01094-o001_t001_niriss_clearp-f356w-sub128_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01094-o001_t001_niriss_clearp-f430m-sub128_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01094-o001_t001_niriss_clearp-f480m-sub128_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01094-o003_t001_niriss_clearp-f444w-sub128_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01193-c1000_t001_nircam_f444w-maskrnd-sub320a430r_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01193-c1000_t001_nircam_f210m-maskrnd-image3_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw02747-o005_t006_nircam_clear-f150w2_i2d.jpg 2
 
< 0.1%
mast:jwst/product/jw01072-o001_t001_nircam_clear-f150w2_i2d.jpg 2
 
< 0.1%
Other values (405706) 405720
> 99.9%
2025-04-05T14:38:37.056929image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3701507
 
13.5%
_ 1622960
 
5.9%
c 1304232
 
4.8%
1 1286557
 
4.7%
r 1255046
 
4.6%
s 1157953
 
4.2%
p 1059021
 
3.9%
a 1020279
 
3.7%
t 868559
 
3.2%
j 811566
 
3.0%
Other values (35) 13249971
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27337651
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3701507
 
13.5%
_ 1622960
 
5.9%
c 1304232
 
4.8%
1 1286557
 
4.7%
r 1255046
 
4.6%
s 1157953
 
4.2%
p 1059021
 
3.9%
a 1020279
 
3.7%
t 868559
 
3.2%
j 811566
 
3.0%
Other values (35) 13249971
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27337651
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3701507
 
13.5%
_ 1622960
 
5.9%
c 1304232
 
4.8%
1 1286557
 
4.7%
r 1255046
 
4.6%
s 1157953
 
4.2%
p 1059021
 
3.9%
a 1020279
 
3.7%
t 868559
 
3.2%
j 811566
 
3.0%
Other values (35) 13249971
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27337651
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3701507
 
13.5%
_ 1622960
 
5.9%
c 1304232
 
4.8%
1 1286557
 
4.7%
r 1255046
 
4.6%
s 1157953
 
4.2%
p 1059021
 
3.9%
a 1020279
 
3.7%
t 868559
 
3.2%
j 811566
 
3.0%
Other values (35) 13249971
48.5%

dataURL
Text

Missing 

Distinct400485
Distinct (%)100.0%
Missing50719
Missing (%)11.2%
Memory size46.4 MiB
2025-04-05T14:38:37.593300image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length94
Median length88
Mean length68.486787
Min length54

Characters and Unicode

Total characters27 427 931
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 485 ?
Unique (%)100.0%

Sample

1st rowmast:JWST/product/jw01076-o113_s000002791_nircam_f322w2-grismr_x1d.fits
2nd rowmast:JWST/product/jw01243-o004_s000001452_nircam_f356w-grismr_x1d.fits
3rd rowmast:JWST/product/jw04233-o005_s000041321_nirspec_f290lp-g395m_s2d.fits
4th rowmast:JWST/product/jw01212-o009_b000000073_nirspec_f290lp-g395h_s2d.fits
5th rowmast:JWST/product/jw01324-o007_s000001336_niriss_f150w-gr150r_x1d.fits
ValueCountFrequency (%)
mast:jwst/product/jw01095-o004_s000000328_niriss_f090w-gr150c_x1d.fits 1
 
< 0.1%
mast:jwst/product/jw01076-o113_s000002791_nircam_f322w2-grismr_x1d.fits 1
 
< 0.1%
mast:jwst/product/jw01243-o004_s000001452_nircam_f356w-grismr_x1d.fits 1
 
< 0.1%
mast:jwst/product/jw04233-o005_s000041321_nirspec_f290lp-g395m_s2d.fits 1
 
< 0.1%
mast:jwst/product/jw01212-o009_b000000073_nirspec_f290lp-g395h_s2d.fits 1
 
< 0.1%
mast:jwst/product/jw02560-o017_s000017470_nirspec_f170lp-g235m_s2d.fits 1
 
< 0.1%
mast:jwst/product/jw03383236001_05201_00003_nis_rateints.fits 1
 
< 0.1%
mast:jwst/product/jw01158-o004_t018_nircam_clear-f212n-nrca3_wfscmb-01.fits 1
 
< 0.1%
mast:jwst/product/jw06434-c1017_s000002225_nircam_f444w-grismc_x1d.fits 1
 
< 0.1%
mast:jwst/product/jw06620001001_04103_00003_mirimage_s2d.fits 1
 
< 0.1%
Other values (400475) 400475
> 99.9%
2025-04-05T14:38:38.355195image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3647538
 
13.3%
s 1671758
 
6.1%
_ 1601940
 
5.8%
1 1449713
 
5.3%
t 1266907
 
4.6%
r 1240278
 
4.5%
i 1027096
 
3.7%
c 990306
 
3.6%
/ 800970
 
2.9%
2 790159
 
2.9%
Other values (35) 12941266
47.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27427931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3647538
 
13.3%
s 1671758
 
6.1%
_ 1601940
 
5.8%
1 1449713
 
5.3%
t 1266907
 
4.6%
r 1240278
 
4.5%
i 1027096
 
3.7%
c 990306
 
3.6%
/ 800970
 
2.9%
2 790159
 
2.9%
Other values (35) 12941266
47.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27427931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3647538
 
13.3%
s 1671758
 
6.1%
_ 1601940
 
5.8%
1 1449713
 
5.3%
t 1266907
 
4.6%
r 1240278
 
4.5%
i 1027096
 
3.7%
c 990306
 
3.6%
/ 800970
 
2.9%
2 790159
 
2.9%
Other values (35) 12941266
47.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27427931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3647538
 
13.3%
s 1671758
 
6.1%
_ 1601940
 
5.8%
1 1449713
 
5.3%
t 1266907
 
4.6%
r 1240278
 
4.5%
i 1027096
 
3.7%
c 990306
 
3.6%
/ 800970
 
2.9%
2 790159
 
2.9%
Other values (35) 12941266
47.2%

dataRights
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.9 MiB
PUBLIC
421724 
EXCLUSIVE_ACCESS
 
29480

Length

Max length16
Median length6
Mean length6.653363
Min length6

Characters and Unicode

Total characters3 002 024
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPUBLIC
2nd rowPUBLIC
3rd rowPUBLIC
4th rowPUBLIC
5th rowPUBLIC

Common Values

ValueCountFrequency (%)
PUBLIC 421724
93.5%
EXCLUSIVE_ACCESS 29480
 
6.5%

Length

2025-04-05T14:38:38.530895image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-05T14:38:38.650848image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
public 421724
93.5%
exclusive_access 29480
 
6.5%

Most occurring characters

ValueCountFrequency (%)
C 510164
17.0%
U 451204
15.0%
I 451204
15.0%
L 451204
15.0%
B 421724
14.0%
P 421724
14.0%
E 88440
 
2.9%
S 88440
 
2.9%
X 29480
 
1.0%
V 29480
 
1.0%
Other values (2) 58960
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3002024
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 510164
17.0%
U 451204
15.0%
I 451204
15.0%
L 451204
15.0%
B 421724
14.0%
P 421724
14.0%
E 88440
 
2.9%
S 88440
 
2.9%
X 29480
 
1.0%
V 29480
 
1.0%
Other values (2) 58960
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3002024
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 510164
17.0%
U 451204
15.0%
I 451204
15.0%
L 451204
15.0%
B 421724
14.0%
P 421724
14.0%
E 88440
 
2.9%
S 88440
 
2.9%
X 29480
 
1.0%
V 29480
 
1.0%
Other values (2) 58960
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3002024
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 510164
17.0%
U 451204
15.0%
I 451204
15.0%
L 451204
15.0%
B 421724
14.0%
P 421724
14.0%
E 88440
 
2.9%
S 88440
 
2.9%
X 29480
 
1.0%
V 29480
 
1.0%
Other values (2) 58960
 
2.0%

mtFlag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size440.8 KiB
False
448050 
True
 
3154
ValueCountFrequency (%)
False 448050
99.3%
True 3154
 
0.7%
2025-04-05T14:38:38.738865image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

srcDen
Unsupported

Missing  Rejected  Unsupported 

Missing451204
Missing (%)100.0%
Memory size3.4 MiB

obsid
Real number (ℝ)

High correlation  Unique 

Distinct451204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1880896 × 108
Minimum71583165
Maximum2.5086059 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:38.866051image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum71583165
5-th percentile87694032
Q12.2963646 × 108
median2.3265315 × 108
Q32.3824239 × 108
95-th percentile2.5036776 × 108
Maximum2.5086059 × 108
Range1.7927743 × 108
Interquartile range (IQR)8605935.5

Descriptive statistics

Standard deviation43425720
Coefficient of variation (CV)0.19846409
Kurtosis4.3212722
Mean2.1880896 × 108
Median Absolute Deviation (MAD)4218009
Skewness-2.3127454
Sum9.8727476 × 1013
Variance1.8857932 × 1015
MonotonicityNot monotonic
2025-04-05T14:38:39.037596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250849246 1
 
< 0.1%
230531914 1
 
< 0.1%
250855097 1
 
< 0.1%
232948977 1
 
< 0.1%
215484344 1
 
< 0.1%
129251214 1
 
< 0.1%
245357627 1
 
< 0.1%
234629551 1
 
< 0.1%
230075259 1
 
< 0.1%
212563399 1
 
< 0.1%
Other values (451194) 451194
> 99.9%
ValueCountFrequency (%)
71583165 1
< 0.1%
71583166 1
< 0.1%
71583167 1
< 0.1%
71583168 1
< 0.1%
71583169 1
< 0.1%
71583170 1
< 0.1%
71583171 1
< 0.1%
71583172 1
< 0.1%
71583173 1
< 0.1%
71583174 1
< 0.1%
ValueCountFrequency (%)
250860592 1
< 0.1%
250860591 1
< 0.1%
250860590 1
< 0.1%
250860589 1
< 0.1%
250860588 1
< 0.1%
250860587 1
< 0.1%
250860586 1
< 0.1%
250860576 1
< 0.1%
250860574 1
< 0.1%
250860573 1
< 0.1%

objID
Real number (ℝ)

High correlation  Unique 

Distinct451204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8707252 × 108
Minimum2.2701044 × 108
Maximum7.3778312 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2025-04-05T14:38:39.191643image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2.2701044 × 108
5-th percentile5.2941701 × 108
Q16.8320832 × 108
median6.9899355 × 108
Q37.3398505 × 108
95-th percentile7.3756233 × 108
Maximum7.3778312 × 108
Range5.1077268 × 108
Interquartile range (IQR)50776726

Descriptive statistics

Standard deviation84942090
Coefficient of variation (CV)0.123629
Kurtosis12.782269
Mean6.8707252 × 108
Median Absolute Deviation (MAD)27263800
Skewness-3.5746896
Sum3.1000987 × 1014
Variance7.2151586 × 1015
MonotonicityNot monotonic
2025-04-05T14:38:39.353688image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
737776078 1
 
< 0.1%
733838415 1
 
< 0.1%
737546727 1
 
< 0.1%
683290106 1
 
< 0.1%
706412579 1
 
< 0.1%
685144617 1
 
< 0.1%
727448191 1
 
< 0.1%
676752536 1
 
< 0.1%
696546198 1
 
< 0.1%
733320160 1
 
< 0.1%
Other values (451194) 451194
> 99.9%
ValueCountFrequency (%)
227010445 1
< 0.1%
227010462 1
< 0.1%
227010591 1
< 0.1%
227010606 1
< 0.1%
227010921 1
< 0.1%
227010926 1
< 0.1%
227010927 1
< 0.1%
227010935 1
< 0.1%
227010936 1
< 0.1%
227010940 1
< 0.1%
ValueCountFrequency (%)
737783120 1
< 0.1%
737783112 1
< 0.1%
737783105 1
< 0.1%
737783090 1
< 0.1%
737783074 1
< 0.1%
737783063 1
< 0.1%
737783047 1
< 0.1%
737783035 1
< 0.1%
737783021 1
< 0.1%
737783006 1
< 0.1%

Interactions

2025-04-05T14:38:13.312994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:48.660531image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.034994image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:53.205261image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.575085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.688897image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:59.813339image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.955795image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:04.371893image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.765649image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.086526image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:10.971525image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:13.508633image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:48.876567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.206708image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:53.393496image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.755518image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.876968image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.039622image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:02.148856image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:04.583085image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.968194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.221622image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:11.182698image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:13.699311image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:49.076913image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.391059image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:53.590378image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.943433image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.063927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.229071image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:02.353588image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:04.802277image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:07.154124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.345535image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:11.367812image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:13.874238image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:49.279030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.559774image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:53.782309image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:56.121386image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.230626image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.415963image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:02.539644image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:05.034359image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:07.376652image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.457457image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:11.567575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.067698image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:49.469195image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.744120image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:54.037134image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:56.303958image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.408870image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.627152image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:02.746461image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:05.234044image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:07.560970image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.559575image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:11.807914image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.255008image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:49.647666image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:51.920363image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:54.228231image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:56.498325image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.572768image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.781714image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:02.966318image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:05.439650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:07.754019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.697001image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.000251image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.442109image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:49.822515image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:52.101184image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:54.504247image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:56.668706image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.746347image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:00.960784image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:03.144597image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:05.642521image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:07.951398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.820540image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.194503image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.620222image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:50.010489image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:52.268422image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:54.707671image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:56.847223image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:58.913507image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.132298image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:03.339276image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:05.845454image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:08.139030image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:09.953313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.401782image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.788412image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:50.184650image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:52.468674image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:54.923403image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.065603image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:59.158795image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.319417image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:03.573686image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.059219image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:08.353258image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:10.057609image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.606578image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:14.907941image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:50.292530image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:52.562649image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.051937image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.191175image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:59.257325image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.415956image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:03.688857image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.179397image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:08.480465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:10.534081image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.728429image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:15.079479image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:50.471401image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:52.744965image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.205011image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.328906image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:59.442749image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.597615image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:03.922903image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.348210image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:08.691215image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:10.659583image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:12.924084image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:15.257367image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:50.649961image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:53.005234image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:55.397517image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:57.529037image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:37:59.637170image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:01.780565image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:04.160124image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:06.567762image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:08.945248image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:10.786918image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-04-05T14:38:13.135957image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-04-05T14:38:39.482188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
calib_leveldataRightsdataproduct_typeem_maxem_mininstrument_nameintentTypemtFlagobjIDobsidproposal_idproposal_typeprovenance_names_decs_rasequence_numbert_exptimet_maxt_mint_obs_releasewavelength_region
calib_level1.0000.0990.7600.3410.3140.6790.4050.1160.2960.4700.6150.3491.0000.1710.2461.0000.0480.2950.2920.2990.148
dataRights0.0991.0000.0490.1010.0350.1430.0280.0190.0710.1010.2600.0900.0870.1450.0941.0000.1050.3840.3840.8570.056
dataproduct_type0.7600.0491.0000.3910.2950.9170.4410.0640.2150.6310.2860.4360.1270.2080.2950.0900.0540.3040.3000.3360.141
em_max0.3410.1010.3911.0000.1420.4830.1720.0330.0870.0110.1200.0870.3840.034-0.0790.0340.3420.1350.1410.1230.087
em_min0.3140.0350.2950.1421.0000.4340.0710.0170.0710.1930.3340.0980.406-0.0000.0180.061-0.3010.3000.3000.1510.033
instrument_name0.6790.1430.9170.4830.4341.0000.5720.3010.1720.3050.2930.2790.5890.1880.2000.1090.0660.2440.2410.2030.233
intentType0.4050.0280.4410.1720.0710.5721.0000.0210.3840.6080.2190.6340.0890.2430.2261.0000.0340.3730.3700.4110.229
mtFlag0.1160.0190.0640.0330.0170.3010.0211.0000.0590.1210.1160.0570.1030.1090.1410.0200.0100.0440.0420.0540.000
objID0.2960.0710.2150.0870.0710.1720.3840.0591.0000.4450.1100.2350.3420.064-0.0360.0290.1040.1370.1310.1740.956
obsid0.4700.1010.6310.0110.1930.3050.6080.1210.4451.0000.4420.3350.3250.038-0.0640.0120.0320.4450.4390.5050.579
proposal_id0.6150.2600.2860.1200.3340.2930.2190.1160.1100.4421.0000.3470.828-0.018-0.023-0.020-0.1070.8480.8440.5710.186
proposal_type0.3490.0900.4360.0870.0980.2790.6340.0570.2350.3350.3471.0000.2840.2520.2550.0690.0360.4290.4290.4060.317
provenance_name1.0000.0870.1270.3840.4060.5890.0890.1030.3420.3250.8280.2841.0000.1470.1791.0000.0401.0001.0001.0000.018
s_dec0.1710.1450.2080.034-0.0000.1880.2430.1090.0640.038-0.0180.2520.1471.0000.458-0.2240.0460.0340.0330.0900.215
s_ra0.2460.0940.295-0.0790.0180.2000.2260.141-0.036-0.064-0.0230.2550.1790.4581.000-0.009-0.247-0.100-0.101-0.0730.374
sequence_number1.0001.0000.0900.0340.0610.1091.0000.0200.0290.012-0.0200.0691.000-0.224-0.0091.000-0.173NaNNaNNaN0.035
t_exptime0.0480.1050.0540.342-0.3010.0660.0340.0100.1040.032-0.1070.0360.0400.046-0.247-0.1731.0000.1250.1270.1760.024
t_max0.2950.3840.3040.1350.3000.2440.3730.0440.1370.4450.8480.4291.0000.034-0.100NaN0.1251.0000.9970.7920.388
t_min0.2920.3840.3000.1410.3000.2410.3700.0420.1310.4390.8440.4291.0000.033-0.101NaN0.1270.9971.0000.7910.393
t_obs_release0.2990.8570.3360.1230.1510.2030.4110.0540.1740.5050.5710.4061.0000.090-0.073NaN0.1760.7920.7911.0000.336
wavelength_region0.1480.0560.1410.0870.0330.2330.2290.0000.9560.5790.1860.3170.0180.2150.3740.0350.0240.3880.3930.3361.000

Missing values

2025-04-05T14:38:15.767062image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-05T14:38:17.137944image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-05T14:38:19.546223image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
0scienceJWSTCALJWSTNIRCAM/GRISMJWSTF322W2;GRISMRINFRAREDIRAS-05248-7007Calibration; Spectrophotometricjw01076-o113_s000002791_nircam_f322w2-grismr81.086467-70.083778spectrumPirzkal, Norbert359699.15990259699.20858396.6312430.04013.0NIRCam LW Grism Baseline Characterization (CAR NIRCam-032)59774.8541671076COMNaNPOLYGON 81.13980245 -70.10170317 81.03323723 -70.1019282 81.03442975 -70.06577854 81.13830085 -70.06582582 81.13980245 -70.10170317mast:JWST/product/jw01076-o113_s000002791_nircam_f322w2-grismr_cal.jpgmast:JWST/product/jw01076-o113_s000002791_nircam_f322w2-grismr_x1d.fitsPUBLICFalseNaN230531914733838415
1scienceJWSTCALJWSTNIRCAM/GRISMJWSTF356W;GRISMRINFRAREDJ1120+0641Galaxy; Quasarsjw01243-o004_s000001452_nircam_f356w-grismr170.0061676.690083spectrumLilly, Simon J.359929.32335959961.231753365.0503140.03980.0Exploring the End of Cosmic Reionization60329.4907521243GTONaNPOLYGON 170.02444323 6.67214966 169.98792647 6.67192467 169.98830271 6.70807468 170.0239607 6.70802746 170.02444323 6.67214966mast:JWST/product/jw01243-o004_s000001452_nircam_f356w-grismr_cal.jpgmast:JWST/product/jw01243-o004_s000001452_nircam_f356w-grismr_x1d.fitsPUBLICFalseNaN250855097737546727
2scienceJWSTCALJWSTNIRSPEC/MSAJWSTF290LP;G395MINFRAREDCEERS-FULL-V2NaNjw04233-o005_s000041321_nirspec_f290lp-g395m214.90966652.872408spectrumde Graaff, Anna G360389.30406860389.6442482844.8342870.05100.0A complete census of the rare, extreme and red: a NIRCam-selected extragalactic community survey with JWST/NIRSpec60390.1679054233GONaNPOLYGON 214.838903171 52.8190293 214.838925626 52.8190293 214.838925626 52.818139786 214.838903171 52.818139786mast:JWST/product/jw04233-o005_s000041321_nirspec_f290lp-g395m_cal.jpgmast:JWST/product/jw04233-o005_s000041321_nirspec_f290lp-g395m_s2d.fitsPUBLICFalseNaN232948977683290106
3scienceJWSTCALJWSTNIRSPEC/MSAJWSTF290LP;G395HINFRAREDGOODSS2009NaNjw01212-o009_b000000073_nirspec_f290lp-g395h53.052791-27.731936spectrumLuetzgendorf, Nora360231.29506860231.3969291750.6662870.05140.0NIRSpec WIDE MOS Survey - GOODS-S60598.0439121212GTONaNPOLYGON 53.049465643 -27.718402892 53.050442906 -27.718402892 53.050442906 -27.718412699 53.049465643 -27.718412699mast:JWST/product/jw01212-o009_b000000073_nirspec_f290lp-g395h_cal.jpgmast:JWST/product/jw01212-o009_b000000073_nirspec_f290lp-g395h_s2d.fitsPUBLICFalseNaN232628095678815355
4scienceJWSTCALJWSTNIRISS/WFSSJWSTGR150R;F150WINFRAREDABELL2744Clusters of Galaxies; Abell clustersjw01324-o007_s000001336_niriss_f150w-gr150r3.594322-30.395694spectrumTreu, Tommaso L.360132.43766560132.5005791288.412800.02200.0Through the looking GLASS: a JWST exploration of galaxy formation and evolution from cosmic dawn to present day60133.6352661324ERSNaNPOLYGON 3.61566792 -30.4145566 3.57252534 -30.41437319 3.5729716 -30.37687049 3.61608797 -30.37708431 3.61566792 -30.4145566mast:JWST/product/jw01324-o007_s000001336_niriss_f150w-gr150r_cal.jpgmast:JWST/product/jw01324-o007_s000001336_niriss_f150w-gr150r_x1d.fitsPUBLICFalseNaN236580081698265567
5scienceJWSTCALJWSTNIRCAM/GRISMJWSTF444W;GRISMRINFRAREDUNKNOWNNaNjw05398-c1010_s000000094_nircam_f444w-grismr7.959913-43.747837spectrumKartaltepe, Jeyhan360620.59349060620.607284203.9993880.04986.0POPPIES: The Public Observation Pure Parallel Infrared Emission-Line Survey60623.1379285398GONaNPOLYGON 7.98504967 -43.7657675 7.93482697 -43.76599251 7.93536107 -43.72984263 7.98436958 -43.72988987 7.98504967 -43.7657675mast:JWST/product/jw05398-c1010_s000000094_nircam_f444w-grismr_cal.jpgmast:JWST/product/jw05398-c1010_s000000094_nircam_f444w-grismr_x1d.fitsPUBLICFalseNaN233405050669659355
6scienceJWSTCALJWSTNIRISS/WFSSJWSTGR150R;F115WINFRAREDABELL370Clusters of Galaxies; Abell clusters; Rich clustersjw01208-c1008_s000000440_niriss_f115w-gr150r39.975354-1.576044spectrumWillott, Chris J.359930.52170659944.347370901.889800.02200.0CANUCS: The CAnadian NIRISS Unbiased Cluster Survey60309.7693751208GTONaNPOLYGON 39.99376963 -1.59490825 39.95654966 -1.59472491 39.95692792 -1.55722214 39.99413888 -1.55743603 39.99376963 -1.59490825mast:JWST/product/jw01208-c1008_s000000440_niriss_f115w-gr150r_cal.jpgmast:JWST/product/jw01208-c1008_s000000440_niriss_f115w-gr150r_x1d.fitsPUBLICFalseNaN229848264656087242
7scienceJWSTCALJWSTNIRCAM/GRISMJWSTF356W;GRISMRINFRAREDJ0224M4711Galaxy; High-redshift galaxies; Quasarsjw02078-o003_s000001471_nircam_f356w-grismr36.110583-47.191500spectrumWang, Feige359802.52411159802.562138472.4183140.03980.0A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): A JWST Quasar Legacy Survey60168.1736922078GONaNPOLYGON 36.13730529 -47.20943023 36.08391442 -47.20965524 36.08448422 -47.17350537 36.13658031 -47.17355262 36.13730529 -47.20943023mast:JWST/product/jw02078-o003_s000001471_nircam_f356w-grismr_cal.jpgmast:JWST/product/jw02078-o003_s000001471_nircam_f356w-grismr_x1d.fitsPUBLICFalseNaN236606818698965426
8scienceJWSTAPTNIRISS/IMAGEJWSTF277WINFRAREDNGC4506NaNjw07763040001_xx201_00001_niriss188.04373513.419356imageWeisz, Daniel R.-1NaNNaN429.4712400.03200.0J-Virgo: A JWST Treasury Survey of the Virgo ClusterNaN7763GO1.0POLYGON 187.94555572005 13.33575317616 187.90737800133 13.33594027052 187.9077379314 13.37332260274 187.94591225228 13.37316179034NaNNaNPUBLICFalseNaN250359236737206211
9scienceJWSTCALJWSTNIRSPEC/MSAJWSTF290LP;G395MINFRAREDGS-MEDIUM-HSTNaNjw01180-o029_s000012571_nirspec_f290lp-g395m53.153721-27.780369spectrumEisenstein, Daniel J.359861.37064459861.7502893063.6662870.05100.0NIRCam-NIRSpec galaxy assembly survey - GOODS-S - part #1athen60227.0312151180GTONaNPOLYGON 53.198558102 -27.761193194 53.199559625 -27.761193194 53.199559625 -27.761252545 53.198558102 -27.761252545mast:JWST/product/jw01180-o029_s000012571_nirspec_f290lp-g395m_cal.jpgmast:JWST/product/jw01180-o029_s000012571_nirspec_f290lp-g395m_s2d.fitsPUBLICFalseNaN229830758737497932
intentTypeobs_collectionprovenance_nameinstrument_nameprojectfilterswavelength_regiontarget_nametarget_classificationobs_ids_ras_decdataproduct_typeproposal_picalib_levelt_mint_maxt_exptimeem_minem_maxobs_titlet_obs_releaseproposal_idproposal_typesequence_numbers_regionjpegURLdataURLdataRightsmtFlagsrcDenobsidobjID
451194scienceJWSTCALJWSTNIRCAM/GRISMJWSTF322W2;GRISMCINFRAREDIRAS-05248-7007Calibration; Spectrophotometricjw01076-o125_s000011202_nircam_f322w2-grismc81.086467-70.083778spectrumPirzkal, Norbert359721.60301559721.64225396.6312430.04013.0NIRCam LW Grism Baseline Characterization (CAR NIRCam-032)59774.8541671076COMNaNPOLYGON 81.13980245 -70.10170317 81.03323723 -70.1019282 81.03442975 -70.06577854 81.13830085 -70.06582582 81.13980245 -70.10170317mast:JWST/product/jw01076-o125_s000011202_nircam_f322w2-grismc_cal.jpgmast:JWST/product/jw01076-o125_s000011202_nircam_f322w2-grismc_x1d.fitsPUBLICFalseNaN230534763691934616
451195scienceJWSTCALJWSTNIRISS/WFSSJWSTGR150C;F090WINFRAREDP330ECalibration; Stray light testjw01095-o007_s000000048_niriss_f090w-gr150c247.89082130.145982spectrumMartel, Andre359713.25814559713.27112153.684600.02200.0NIRISS Stray Light Contamination61115.7507181095COMNaNPOLYGON 247.91210443 30.12711601 247.86908791 30.12729927 247.8695166 30.16480212 247.91253982 30.16458816 247.91210443 30.12711601mast:JWST/product/jw01095-o007_s000000048_niriss_f090w-gr150c_cal.jpgmast:JWST/product/jw01095-o007_s000000048_niriss_f090w-gr150c_x1d.fitsEXCLUSIVE_ACCESSFalseNaN250850893737780416
451196scienceJWSTCALJWSTNIRCAM/IMAGEJWSTF444WINFRAREDUNKNOWNNaNjw03990142001_02201_00004_nrcalong352.422137-15.394077imageMorishita, Takahiro260494.33449060494.3784813736.3963880.04986.0A NIRCam Pure-Parallel Imaging Survey of Galaxies Across the Universe60494.8577083990GONaNPOLYGON 352.446029777 -15.405352854 352.410428354 -15.416679649 352.398581656 -15.383165445 352.433630343 -15.371118589mast:JWST/product/jw03990142001_02201_00004_nrcalong_cal.jpgmast:JWST/product/jw03990142001_02201_00004_nrcalong_cal.fitsPUBLICFalseNaN218181229670499804
451197scienceJWSTCALJWSTNIRSPEC/MSAJWSTF100LP;G140MINFRAREDSUSPENSE_v10_correct_coordsNaNjw02110-o001_s000129015_nirspec_f100lp-g140m150.5055502.461475spectrumKriek, Mariska360311.25059160311.6459618753.334700.05000.0Ultra-deep continuum spectroscopy of quiescent galaxies at 1.0<z<2.5: chemical abundances and stellar kinematics60311.8860762110GONaNPOLYGON 150.488363855 2.46315548 150.488774139 2.46315548 150.488774139 2.463018706 150.488363855 2.463018706mast:JWST/product/jw02110-o001_s000129015_nirspec_f100lp-g140m_cal.jpgmast:JWST/product/jw02110-o001_s000129015_nirspec_f100lp-g140m_s2d.fitsPUBLICFalseNaN232882252671677749
451198scienceJWSTAPTNIRSPEC/SLITJWSTF290LP;G395MINFRAREDSNIc-BLNaNjw08105043001_xx104_00001_nirspec359.9998550.000165spectrumShahbandeh, Melissa-1NaNNaN311.6002870.05100.0Are Supernovae Dust Builders or Wreckers? Lets Settle This!NaN8105GO4.0POLYGON 0.00015875798 -0.00026515298 359.99947025106 0.00052314339 359.99955222542 0.00059599854 0.00024073499 -0.00019225863NaNNaNPUBLICFalseNaN250365631736709734
451199scienceJWSTCALJWSTNIRCAM/GRISMJWSTF356W;GRISMRINFRAREDJ0224M4711Galaxy; High-redshift galaxies; Quasarsjw02078-o003_s000000918_nircam_f356w-grismr36.110583-47.191500spectrumWang, Feige359802.52411159802.562138472.4183140.03980.0A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): A JWST Quasar Legacy Survey60168.1736922078GONaNPOLYGON 36.13730529 -47.20943023 36.08391442 -47.20965524 36.08448422 -47.17350537 36.13658031 -47.17355262 36.13730529 -47.20943023mast:JWST/product/jw02078-o003_s000000918_nircam_f356w-grismr_cal.jpgmast:JWST/product/jw02078-o003_s000000918_nircam_f356w-grismr_x1d.fitsPUBLICFalseNaN236604668698963673
451200scienceJWSTCALJWSTNIRCAM/GRISMJWSTF356W;GRISMRINFRAREDJ0148+0600Galaxy; Quasarsjw01243-o006_s000003704_nircam_f356w-grismr27.1568296.005558spectrumLilly, Simon J.359958.49575359959.283417365.0503140.03980.0Exploring the End of Cosmic Reionization60419.8204741243GTONaNPOLYGON 27.17509827 5.98738303 27.13857402 5.98758025 27.1389918 6.02352749 27.17466375 6.02359138 27.17509827 5.98738303mast:JWST/product/jw01243-o006_s000003704_nircam_f356w-grismr_cal.jpgmast:JWST/product/jw01243-o006_s000003704_nircam_f356w-grismr_x1d.fitsPUBLICFalseNaN250854196737763407
451201scienceJWSTAPTNIRSPEC/MSAJWSTF290LP;G395MINFRAREDemerald_gdn_tgt_ptg_6NaNjw07935006001_xx10f_00002_nirspec189.13070862.254983spectrumSun, Fengwu-1NaNNaN1167.1112870.05100.0Efficient Measurement of the Emergence Rate of AGN in Legacy Deep FieldNaN7935GO15.0POLYGON 189.13745554882 62.30239522345 189.22039158531 62.25568252834 189.12104231634 62.21693525775 189.03664659996 62.26304049865NaNNaNPUBLICFalseNaN250363511736704031
451202scienceJWSTCALJWSTMIRI/CORONJWSTF1550C;4QPM_1550INFRAREDNEW-EPS-MUS-OFFSET-G9Calibration; Point spread functionjw06797008001_04101_00004_mirimage184.359072-67.968354imageBeichman, Charles A.260727.19790660727.2323022971.79215110.015890.0Confirming the presence of a gas giant planet orbiting a nearby solar-type star60727.5964126797DDNaNPOLYGON 184.379254576 -67.966135504 184.37223751 -67.959741018 184.393978507 -67.95644455 184.401019737 -67.962808782mast:JWST/product/jw06797008001_04101_00004_mirimage_rateints.jpgmast:JWST/product/jw06797008001_04101_00004_mirimage_rateints.fitsPUBLICFalseNaN242871579733331409
451203scienceJWSTCALJWSTNIRISS/WFSSJWSTGR150C;F090WINFRAREDP330ECalibration; Stray light testjw01095-o004_s000000328_niriss_f090w-gr150c247.89082130.145982spectrumMartel, Andre359713.18641759713.19939453.684600.02200.0NIRISS Stray Light Contamination61115.7507181095COMNaNPOLYGON 247.91210443 30.12711602 247.86908791 30.12729928 247.8695166 30.16480212 247.91253982 30.16458816 247.91210443 30.12711602mast:JWST/product/jw01095-o004_s000000328_niriss_f090w-gr150c_cal.jpgmast:JWST/product/jw01095-o004_s000000328_niriss_f090w-gr150c_x1d.fitsEXCLUSIVE_ACCESSFalseNaN250849246737776078